Artificial intelligence-powered software detected more than half of the liver metastases overlooked by radiologists on contrast-enhanced CT. Issue 163 (June 2023)

• Record Type: Journal Article
• Authors: Nakai, Hirotsugu; Sakamoto, Ryo; Kakigi, Takahide; Coeur, Christophe; Isoda, Hiroyoshi; Nakamoto, Yuji
• Is Part Of: European journal of radiology. Issue 163(2023)
• Publication Date: 2023-06
• Subjects: Diagnostic errors -- Liver neoplasms -- Multidetector computer tomography -- Artificial intelligence -- Deep learning; AI Artificial intelligence -- CECT Contrast-enhanced computed tomography; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2023.110823
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Per-lesion sensitivity of the software to liver metastases overlooked by radiologists was 55.0%. Per-patient sensitivity to liver metastases overlooked by radiologists was 53.7%. The AI-powered software may assist radiologists in detecting liver metastases. Abstract: Purpose: To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting liver metastases, especially those overlooked by radiologists. Methods: Records of 746 patients diagnosed with liver metastases (November 2010–September 2017) were reviewed. Images from when radiologists first diagnosed liver metastases were reviewed, and prior contrast-enhanced CT (CECT) images were checked for availability. Two abdominal radiologists classified the lesions into overlooked lesions (all metastases missed by radiologists on prior CECT) and detected lesions (all metastases if any of them were correctly identified and invisible on prior CECT or those with no prior CECT). Finally, images from 137 patients were identified, 68 of which were classified as "overlooked cases." The same radiologists created the ground truth for these lesions and compared them with the software's output at 2-month intervals. The primary endpoint was the sensitivity in detecting all liver lesion types, liver metastases, and liver metastases overlooked by radiologists. Results: The software successfully processed images from 135 patients. The per-lesion sensitivity for all liver lesion types, liver metastases, and liver metastases overlooked by radiologists was 70.1%, 70.8%, and 55.0%, respectively. The software detected liver metastases in 92.7% and 53.7% of patients in detected and overlooked cases, respectively. The average number of false positives was 0.48 per patient. Conclusion: The AI-powered software detected more than half of liver metastases overlooked by radiologists while maintaining a relatively low number of false positives. Our results suggest the potential of AI-powered software in reducing the frequency of overlooked liver metastases when used in conjunction with the radiologists' clinical interpretation.